Statistics for Corpus Linguists
  • Overview
  • Fundamentals
  • Introduction to R
  • NLP
  • Statistics
  • Models
  • Machine Learning
  1. 7. Machine Learning
  2. 7.5 Clustering
  • 7. Machine Learning
    • 7.1 Tree-based methods
    • 7.2 Gradient boosting
    • 7.3 PCA
    • 7.4 EFA
    • 7.5 Clustering

On this page

  • Recommended reading
  • Preparation
  • Clustering algorithms
    • \(k\)-means
    • Partitioning around medoids (PAM)
    • Hierarchical agglomerative clustering
  1. 7. Machine Learning
  2. 7.5 Clustering

7.5 Clustering

Author
Affiliation

Vladimir Buskin

Catholic University of Eichstätt-Ingolstadt

Recommended reading

James et al. (2021): Chapter 12

Hastie, Tibshirani, and Friedman (2017): Chapters 14.3.6, 14.3.10 & 14.3.12

Preparation

Clustering algorithms

Warning

This page is still under construction. More content will be added soon!

\(k\)-means

Partitioning around medoids (PAM)

Hierarchical agglomerative clustering

Hastie, Trevor, Robert Tibshirani, and Jerome H. Friedman. 2017. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. New York, NY: Springer.
James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2021. An Introduction to Statistical Learning: With Applications in r. New York: Springer. https://doi.org/10.1007/978-1-0716-1418-1.
7.4 EFA